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An autonomous compartmental model for accelerating epidemics

In Fall 2020, several European countries reported rapid increases in COVID-19 cases along with growing estimates of the effective reproduction rates. Such an acceleration in epidemic spread is usually attributed to time-dependent effects, e.g. human travel, seasonal behavioral changes, mutations of...

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Detalles Bibliográficos
Autores principales: Budanur, Nazmi Burak, Hof, Björn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9292088/
https://www.ncbi.nlm.nih.gov/pubmed/35849565
http://dx.doi.org/10.1371/journal.pone.0269975
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author Budanur, Nazmi Burak
Hof, Björn
author_facet Budanur, Nazmi Burak
Hof, Björn
author_sort Budanur, Nazmi Burak
collection PubMed
description In Fall 2020, several European countries reported rapid increases in COVID-19 cases along with growing estimates of the effective reproduction rates. Such an acceleration in epidemic spread is usually attributed to time-dependent effects, e.g. human travel, seasonal behavioral changes, mutations of the pathogen etc. In this case however the acceleration occurred when counter measures such as testing and contact tracing exceeded their capacity limit. Considering Austria as an example, here we show that this dynamics can be captured by a time-independent, i.e. autonomous, compartmental model that incorporates these capacity limits. In this model, the epidemic acceleration coincides with the exhaustion of mitigation efforts, resulting in an increasing fraction of undetected cases that drive the effective reproduction rate progressively higher. We demonstrate that standard models which does not include this effect necessarily result in a systematic underestimation of the effective reproduction rate.
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spelling pubmed-92920882022-07-19 An autonomous compartmental model for accelerating epidemics Budanur, Nazmi Burak Hof, Björn PLoS One Research Article In Fall 2020, several European countries reported rapid increases in COVID-19 cases along with growing estimates of the effective reproduction rates. Such an acceleration in epidemic spread is usually attributed to time-dependent effects, e.g. human travel, seasonal behavioral changes, mutations of the pathogen etc. In this case however the acceleration occurred when counter measures such as testing and contact tracing exceeded their capacity limit. Considering Austria as an example, here we show that this dynamics can be captured by a time-independent, i.e. autonomous, compartmental model that incorporates these capacity limits. In this model, the epidemic acceleration coincides with the exhaustion of mitigation efforts, resulting in an increasing fraction of undetected cases that drive the effective reproduction rate progressively higher. We demonstrate that standard models which does not include this effect necessarily result in a systematic underestimation of the effective reproduction rate. Public Library of Science 2022-07-18 /pmc/articles/PMC9292088/ /pubmed/35849565 http://dx.doi.org/10.1371/journal.pone.0269975 Text en © 2022 Budanur, Hof https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Budanur, Nazmi Burak
Hof, Björn
An autonomous compartmental model for accelerating epidemics
title An autonomous compartmental model for accelerating epidemics
title_full An autonomous compartmental model for accelerating epidemics
title_fullStr An autonomous compartmental model for accelerating epidemics
title_full_unstemmed An autonomous compartmental model for accelerating epidemics
title_short An autonomous compartmental model for accelerating epidemics
title_sort autonomous compartmental model for accelerating epidemics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9292088/
https://www.ncbi.nlm.nih.gov/pubmed/35849565
http://dx.doi.org/10.1371/journal.pone.0269975
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